| Summary: | 碩士 === 中原大學 === 資訊管理研究所 === 93 === Because of the popularity of e-commerce and the vigorous growth of the internet networking, email has became the most common way of communication of commercial exchange gradually. This utility has already been abused gradually at present. Receivers have to spend large of time to deal with a large number of commercial emails. Not only cause the waste of time, but also hinder the receiving of the important mail. Due to the bulk sending of commercial emails, the email service provider must consume huge strength and resources to deal with the jams of the internetworking. Besides hindering from the normal service of network connecting, it has also already damaged the using of the public network environment seriously.
Previous researches proposed many kinds of the heuristic classification approach for spam filtering. Far more beyond the circumstances in order to avoid from the detecting and filtering of antispam utilities, spammer have already evolved their model into content rareness, graphicalizing, reducing the link of url, interval sending etc. To react these circumstances, those classification approaches still need to modify and update contiguously.
Our research proposes a spam filtering prototype system which applying the artificial immune system approach. The design of Artificial Immune system is to imitate the process of the human immune system. According to the human immunity, AIS has the abilities of recognition and adaptation. When pathogens invade the human immunity, macrophages as the vanguard in the frontline compose pathogens into antigens. Consider the transcribed characteristics of spam as the antigens and the rules of antispam as the antibodies. Spam has been considered as recognized when the antibodies bind with the corresponding antigens. Imitating the process of the clonal selection, the active antibodies could be cloned and mutated to produce new antibodies. These antibodies can filter new model spam that antispam utilities can’t detect.
Assess the spam precision rate and legitimate recall rate synthetically. The result of our research shows that our prototype system has better performance than the SpamAssassin in the experiments of both spam corpus test and daily mail test. Our research concludes that using Immunogenetics as the kernel of AIS can solve the question of multi-object optimization that original Genetic Algorithm can’t. This ‘Adaptability’ will be more efficiency in dealing the new model spam classification.
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